To address these issues we consider an alternative mechanism for load balancing in a community energy system, which focuses on encouraging users to handle load-balancing by actively altering their behavior. We assume that each household has an agent program running on their smart meter, into which they can input their preferred time-slots for when they would like to run high-powered but time-flexible appliances such as washing machines, dishwashers and tumble dryers. The aim is then to allocate actual time-slots to each household agent for when they run their appliances. With this approach a community could collectively own a source of renewable energy, reducing the initial cost, and then use high-powered appliances when the energy is available, as opposed to sharing energy that they generated individually [1]}. On the one hand this is a classic multi-objective optimisation problem of reducing peak consumption (the maximum amount of energy demanded in a time-slot) while satisfying each households' preferences as far as possible. On the other hand issues of fairness are central. If households are to be motivated to use the mechanism then they will need to perceive the resulting allocation of time-slots to households as being fair (distributive justice [2]}). It is therefore important that agents are able to maximise their collective performance, but also all individuals should be able to perform well individually. Furthermore, households will need to be able to understand why some of their time-slot preferences have not been met and why the preferences of some other households may have been met instead. In other words, they need to perceive the allocation procedure as treating them fairly (procedural justice [3]}). Many agent-based approaches to resource allocation, including our mechanism, are designed so that they can operate as a socio-technical system such that the agents representing users could be taken over by those users. This requires that the agents possible actions and the reasons behind the decisions they make are easily explainable and interpretable to a typical user, which inherently facilitates procedural justice [4]}, [5]}.
